Correspondence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Todorich, Bozho MD, PhD1; Thanos, Aristomenis MD1; Yonekawa, Yoshihiro MD1; Thomas, Benjamin J. MD1; Faia, Lisa J. MD1; Chang, Emmanuel MD2; Shulman, Julia MD3; Olsen, Karl R. MD4; Blair, Michael P. MD5; Shapiro, Michael P. MD5; Ferrone, Philip MD6; Vajzovic, Lejla MD7; Toth, Cynthia A. MD7; Lee, Thomas C. MD8; Robinson, Joshua MD9; Hubbard, Baker MD9; Kondo, Hiroyuki MD10; Besirli, Cagri G. MD, PhD11; Nudleman, Eric MD, PhD12; Wong, Sui Chien MD13,14,15; Kusaka, Shunji MD16; Walsh, Mark MD, PhD17; Chan, R. V. Paul MD18; Berrocal, Audina MD19; Caputo, George MD20; Murray, Timothy G. MD, MBA21; Sears, Jonathan MD22; Schunemann, Roberto MD23; Harper, Clio A. III MD24; Kychental, Andres MD25; Dorta, Paola MD26; Cernichiaro-Espinosa, Linda A. MD27; Wu, Wei-Chi MD, PhD28,29; Campbell, J. Peter MD, MPH, MA30; Martinez-Castellanos, Maria A. MD31,32; Quiroz-Mercado, Hugo MD32; Hayashi, Hidyuki MD33; Quiram, Polly MD, PhD34; Amphornphruet, Atchara MD35; Hartnett, Mary E. MD36; Tsui, Irena MD37; Ells, Anna MD38; John, Vishak MD39; Moshfeghi, Darius MD40; Capone, Antonio Jr MD1; Drenser, Kimberly A. MD, PhD1; Trese, Michael T. MD1 Author Information
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it